Bar Plot of cook's distance to detect observations that strongly influence fitted values of the model.

ols_plot_cooksd_bar(model, type = 1, print_plot = TRUE)

model | An object of class |
---|---|

type | An integer between 1 and 5 selecting one of the 6 methods for computing the threshold. |

print_plot | logical; if |

`ols_plot_cooksd_bar`

returns a list containing the
following components:

a `data.frame`

with observation number and `cooks distance`

that exceed `threshold`

`threshold`

for classifying an observation as an outlier

Cook's distance was introduced by American statistician R Dennis Cook in
1977. It is used to identify influential data points. It depends on both the
residual and leverage i.e it takes it account both the *x* value and
*y* value of the observation.

Steps to compute Cook's distance:

Delete observations one at a time.

Refit the regression model on remaining \(n - 1\) observations

examine how much all of the fitted values change when the ith observation is deleted.

A data point having a large cook's d indicates that the data point strongly influences the fitted values. There are several methods/formulas to compute the threshold used for detecting or classifying observations as outliers and we list them below.

**Type 1**: 4 / n**Type 2**: 4 / (n - k - 1)**Type 3**: ~1**Type 4**: 1 / (n - k - 1)**Type 5**: 3 * mean(Vector of cook's distance values)

where **n** and **k** stand for

**n**: Number of observations**k**: Number of predictors

`ols_cooksd_barplot()`

has been deprecated. Instead use `ols_plot_cooksd_bar()`

.

[ols_plot_cooksd_chart()]